SoroushVahidi/scidocs huggingface dataset:
Vehicle Detection and Tracking in Car Video Based on Motion Model
(2011)

This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.
The Weak Byzantine Generals Problem
(1983)

The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an "incorrect" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures. Categories and Subject Descnptors: D.4.5 [Operating Systems]: Reliability, F.2.m [Analysis of Algorithms and Problem Complexity]: Mzscellaneous General Terms: Reliabthty Addmonal
Action and Interaction Recognition in First-Person Videos
(2014)

In this work, we evaluate the performance of the popular dense trajectories approach on first-person action recognition datasets. A person moving around with a wearable camera will actively interact with humans and objects and also passively observe others interacting. Hence, in order to represent real-world scenarios, the dataset must contain actions from first-person perspective as well as third-person perspective. For this purpose, we introduce a new dataset which contains actions from both the perspectives captured using a head-mounted camera. We employ a motion pyramidal structure for grouping the dense trajectory features. The relative strengths of motion along the trajectories are used to compute different bag-of-words descriptors and concatenated to form a single descriptor for the action. The motion pyramidal approach performs better than the baseline improved trajectory descriptors. The method achieves 96.7% on the JPL interaction dataset and 61.8% on our NUS interaction dataset. The same is used to detect actions in long video sequences and achieves average precision of 0.79 on JPL interaction dataset.
Wearable Electronics and Smart Textiles: A Critical Review
(2014)

Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.
Customers ’ Behavior Prediction Using Artificial Neural Network
(2013)

In this paper, customer restaurant preference is predicted based on social media location check-ins. Historical preferences of the customer and the influence of the customer’s social network are used in combination with the customer’s mobility characteristics as inputs to the model. As the popularity of social media increases, more and more customer comments and feedback about products and services are available online. It not only becomes a way of sharing information among friends in the social network but also forms a new type of survey which can be utilized by business companies to improve their existing products, services, and market analysis. Approximately 121,000 foursquare restaurant check-ins in the Greater New York City area are used in this research. Artificial neural networks (ANN) and support vector machine (SVM) are developed to predict the customers’ behavior regarding restaurant preferences. ANN provides 93.13% average accuracy across investigated customers, compared to only 54.00% for SVM with a sigmoid kernel function.
Scene Understanding by Reasoning Stability and Safety
(2014)

This paper presents a new perspective for 3D scene understanding by reasoning object stability and safety using intuitive mechanics. Our approach utilizes a simple observation that, by human design, objects in static scenes should be stable in the gravity field and be safe with respect to various physical disturbances such as human activities. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Given a 3D point cloud captured for a static scene by depth cameras, our method consists of three steps: (i) recovering solid 3D volumetric primitives from voxels; (ii) reasoning stability by grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior; and (iii) reasoning safety by evaluating the physical risks for objects under physical disturbances, such as human activity, wind or earthquakes. We adopt a novel intuitive physics model and represent the energy landscape of each primitive and object in the scene by a disconnectivity graph (DG). We construct a contact graph with nodes being 3D volumetric primitives and edges representing the supporting relations. Then we adopt a Swendson–Wang Cuts algorithm to partition the contact graph into groups, each of which is a stable object. In order to detect unsafe objects in a static scene, our method further infers hidden and situated causes (disturbances) in the scene, and then introduces intuitive physical mechanics to predict possible effects (e.g., falls) as consequences of the disturbances. In experiments, we demonstrate that the algorithm achieves a substantially better performance for (i) object segmentation, (ii) 3D volumetric recovery, and (iii) scene understanding with respect to other state-of-the-art methods. We also compare the safety prediction from the intuitive mechanics model with human judgement.
Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography.
(2007)

Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the organization of the human brain in the normal state and in clinical disorders.
The role of the self in mindblindness in autism
(2011)

Since its inception the 'mindblindness' theory of autism has greatly furthered our understanding of the core social-communication impairments in autism spectrum conditions (ASC). However, one of the more subtle issues within the theory that needs to be elaborated is the role of the 'self'. In this article, we expand on mindblindness in ASC by addressing topics related to the self and its central role in the social world and then review recent research in ASC that has yielded important insights by contrasting processes relating to both self and other. We suggest that new discoveries lie ahead in understanding how self and other are interrelated and/or distinct, and how understanding atypical self-referential and social-cognitive mechanisms may lead to novel ideas as to how to facilitate social-communicative abilities in ASC.
A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments
(2018)

The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow more ecologically-valid measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have
Programming with Abstract Data Types
(1974)

The motivation behind the work in very-high-level languages is to ease the programming task by providing the programmer with a language containing primitives or abstractions suitable to his problem area. The programmer is then able to spend his effort in the right place; he concentrates on solving his problem, and the resulting program will be more reliable as a result. Clearly, this is a worthwhile goal.
 Unfortunately, it is very difficult for a designer to select in advance all the abstractions which the users of his language might need. If a language is to be used at all, it is likely to be used to solve problems which its designer did not envision, and for which the abstractions embedded in the language are not sufficient.
 This paper presents an approach which allows the set of built-in abstractions to be augmented when the need for a new data abstraction is discovered. This approach to the handling of abstraction is an outgrowth of work on designing a language for structured programming. Relevant aspects of this language are described, and examples of the use and definitions of abstractions are given.
The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings
(2011)

The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism. 2010 Elsevier Ltd. All rights reserved.
Designing for Exploratory Search on Touch Devices
(2015)

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
(1996)

A useful starting point for designing advanced graphical user interfaces is the Visual InformationSeeking Mantra: Overview first, zoom and filter, then details-on-demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. This paper offers a task by data type taxonomy with seven data types (1-, 2-, 3-dimensional data, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extract).
TopicPanorama: A Full Picture of Relevant Topics
(2014)

This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.
A user-centric evaluation framework for recommender systems
(2011)

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.
Interaction Design for Recommender Systems
(2002)

1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.
Driving cycle-based design optimization of interior permanent magnet synchronous motor drives for electric vehicle application
(2014)

The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.
Learning From Positive and Unlabeled Data: A Survey
(2018)

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
Machine Learning Techniques in ADAS: A Review
(2018)

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.
Radian: Visual Exploration of Traceroutes
(2018)

Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.
DifNet: Semantic Segmentation by Diffusion Networks
(2018)

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.